Results 11 to 20 of about 19,032 (297)
Dynamic Contextualized Word Embeddings [PDF]
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021 Static word embeddings that represent words by a single vector cannot capture the variability of word meaning in different linguistic and extralinguistic contexts. Building on prior work on contextualized and dynamic word embeddings, we introduce dynamic contextualized word embeddings that represent words as a function of both linguistic and ...
Hofmann, V +2 more
core +6 more sources
AutoExtend: Combining Word Embeddings with Semantic Resources
Computational Linguistics, 2017 We present AutoExtend, a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning word embeddings that incorporate the semantic information from the resource.
Sascha Rothe, Hinrich Schütze
doaj +3 more sources
Creating Welsh Language Word Embeddings [PDF]
Applied Sciences, 2021 Word embeddings are representations of words in a vector space that models semantic relationships between words by means of distance and direction. In this study, we adapted two existing methods, word2vec and fastText, to automatically learn Welsh word ...
Padraig Corcoran +4 more
doaj +2 more sources
Word embeddings as autonomous predictors in materials design—the effect of inherent variability on information transfer [PDF]
Journal of CheminformaticsWe propose that word embeddings of atoms derived from scientific literature are revisited as autonomous machine learning predictors in materials design.
Jana Radaković +2 more
doaj +2 more sources
Relational Word Embeddings [PDF]
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019 While word embeddings have been shown to implicitly encode various forms of attributional knowledge, the extent to which they capture relational information is far more limited. In previous work, this limitation has been addressed by incorporating relational knowledge from external knowledge bases when learning the word embedding.
José Camacho-Collados +2 more
openaire +4 more sources
Socialized Word Embeddings [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Word embeddings have attracted a lot of attention. On social media, each user’s language use can be significantly affected by the user’s friends. In this paper, we propose a socialized word embedding algorithm which can consider both user’s personal characteristics of language use and the user’s social relationship on social media.
Ziqian Zeng +3 more
openaire +2 more sources
Training and intrinsic evaluation of lightweight word embeddings for the clinical domain in Spanish [PDF]
Frontiers in Artificial Intelligence, 2022 Resources for Natural Language Processing (NLP) are less numerous for languages different from English. In the clinical domain, where these resources are vital for obtaining new knowledge about human health and diseases, creating new resources for the ...
Carolina Chiu +10 more
doaj +2 more sources
Proceedings of the AAAI Conference on Artificial Intelligence, 2015 Most word embedding models typically represent each word using a single vector, which makes these models indiscriminative for ubiquitous homonymy and polysemy. In order to enhance discriminativeness, we employ latent topic models to assign topics for each word in the text corpus, and learn topical word embeddings (TWE) based on both ...
Yang Liu 0005 +3 more
openaire +2 more sources
Using word embeddings to investigate cultural biases [PDF]
British Journal of Social Psychology, 2023 Kevin Durrheim, Maria Schuld
exaly +2 more sources
Comparing general and specialized word embeddings for biomedical named entity recognition [PDF]
PeerJ Computer Science, 2021 Increased interest in the use of word embeddings, such as word representation, for biomedical named entity recognition (BioNER) has highlighted the need for evaluations that aid in selecting the best word embedding to be used.
Rigo E. Ramos-Vargas +2 more
doaj +2 more sources